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PubMed2026

The impact of artificial intelligence on critical thinking and clinical reasoning in health professions education: A systematic review and meta-analysis.

BACKGROUND: Critical thinking and clinical reasoning underpin healthcare professionals' ability to navigate uncertainties and deliver safe and effective care. With artificial intelligence (AI) advancement and growing adoption, AI-based educational tools are increasingly used to support these cognitive competencies' development. OBJECTIVE: To synthesize randomised and controlled clinical trials on AI-based educational tools in health professions education and examine their effects on critical thinking and clinical reasoning among health professions students. METHODS: Six electronic databases were searched from January 1, 2014 to July 28, 2025 was reviewed: PubMed, Cochrane Central Register of Controlled Trials, CINAHL, Scopus, Embase and Web of Science. Two independent reviewers performed data extraction and quality assessment using standardized JBI checklists. The GRADE approach was used to assess the certainty of evidence. Studies were pooled via random-effects meta-analyses or narrative syntheses. RESULTS: Fourteen randomised controlled trials and seven controlled clinical trials were included (n = 21). Meta-analyses revealed small to medium effect sizes for the surrogate clinical reasoning outcomes of performance-based assessment scores (SMD 0.68; 95% CI [0.38, 0.98], p-value = 0.00; I2 = 38%) and knowledge test scores (SMD 0.39; 95% CI [0.09, 0.69], p-value = 0.01; I2 = 79%). Critical thinking and clinical reasoning skills and dispositions were narratively synthesized, with majority of included studies favouring AI-based interventions but the evidence had low to very low certainty. CONCLUSION: AI-based educational interventions may improve critical thinking and clinical reasoning among health profession students, but the evidence is very uncertain. This review offers preliminary insights but does not allow identification of optimal interventions or discipline-specific recommendations due to small sample sizes and substantial intervention heterogeneity. Further research is required to draw definitive conclusions. PROTOCOL REGISTRATION: CRD42025634074.

Humans↗
PubMed2005

Neural correlates of superior intelligence: stronger recruitment of posterior parietal cortex.

General intelligence (g) is a common factor in diverse cognitive abilities and a major influence on life outcomes. Neuroimaging studies in adults suggest that the lateral prefrontal and parietal cortices play a crucial role in related cognitive activities including fluid reasoning, the control of attention, and working memory. Here, we investigated the neural bases for intellectual giftedness (superior-g) in adolescents, using fMRI. The participants consisted of a superior-g group (n = 18, mean RAPM = 33.9 +/- 0.8, >99%) from the national academy for gifted adolescents and the control group (n = 18, mean RAPM = 22.8 +/- 1.6, 60%) from local high schools in Korea (mean age = 16.5 +/- 0.8). fMRI data were acquired while they performed two reasoning tasks with high and low g-loadings. In both groups, the high g-loaded tasks specifically increased regional activity in the bilateral fronto-parietal network including the lateral prefrontal, anterior cingulate, and posterior parietal cortices. However, the regional activations of the superior-g group were significantly stronger than those of the control group, especially in the posterior parietal cortex. Moreover, regression analysis revealed that activity of the superior and intraparietal cortices (BA 7/40) strongly covaried with individual differences in g (r = 0.71 to 0.81). A correlated vectors analysis implicated bilateral posterior parietal areas in g. These results suggest that superior-g may not be due to the recruitment of additional brain regions but to the functional facilitation of the fronto-parietal network particularly driven by the posterior parietal activation.

Adolescent↗
PubMed2004

Head size and intelligence, learning, nutritional status and brain development. Head, IQ, learning, nutrition and brain.

This multifactorial study investigates the interrelationships between head circumference (HC) and intellectual quotient (IQ), learning, nutritional status and brain development in Chilean school-age children graduating from high school, of both sexes and with high and low IQ and socio-economic strata (SES). The sample consisted of 96 right-handed healthy students (mean age 18.0 +/- 0.9 years) born at term. HC was measured both in the children and their parents and was expressed as Z-score (Z-HC). In children, IQ was determined by means of the Wechsler Intelligence Scale for Adults-Revised (WAIS-R), scholastic achievement (SA) through the standard Spanish language and mathematics tests and the academic aptitude test (AAT) score, nutritional status was assessed through anthropometric indicators, brain development was determined by magnetic resonance imaging (MRI) and SES applying the Graffar modified method. Results showed that microcephalic children (Z-HC < or = 2 S.D.) had significantly lower values mainly for brain volume (BV), parental Z-HC, IQ, SA, AAT, birth length (BL) and a significantly higher incidence of undernutrition in the first year of life compared with their macrocephalic peers (Z-HC > 2S.D.). Multiple regression analysis revealed that BV, parental Z-HC and BL were the independent variables with the greatest explanatory power for child's Z-HC variance (r(2) = 0.727). These findings confirm the hypothesis formulated in this study: (1) independently of age, sex and SES, brain parameters, parental HC and prenatal nutritional indicators are the most important independent variables that determine HC and (2) microcephalic children present multiple disorders not only related to BV but also to IQ, SA and nutritional background.

Adolescent↗
PubMed2005

Seizure-related factors and non-verbal intelligence in children with epilepsy. A population-based study from Western Norway.

PURPOSE: To study the relationship between seizure-related factors, non-verbal intelligence, and socio-economic status (SES) in a population-based sample of children with epilepsy. METHODS: The latest ILAE International classifications of epileptic seizures and syndromes were used to classify seizure types and epileptic syndromes in all 6-12 year old children (N=198) with epilepsy in Hordaland County, Norway. The children had neuropediatric and EEG examinations. Of the 198 patients, demographic characteristics were collected on 183 who participated in psychological studies including Raven matrices. 126 healthy controls underwent the same testing. Severe non-verbal problems (SNVP) were defined as a Raven score at or <10th percentile. RESULTS: Children with epilepsy were highly over-represented in the lowest Raven percentile group, whereas controls were highly over-represented in the higher percentile groups. SNVP were present in 43% of children with epilepsy and 3% of controls. These problems were especially common in children with remote symptomatic epilepsy aetiology, undetermined epilepsy syndromes, myoclonic seizures, early seizure debut, high seizure frequency and in children with polytherapy. Seizure-related characteristics that were not usually associated with SNVP were idiopathic epilepsies, localization related (LR) cryptogenic epilepsies, absence and simple partial seizures, and a late debut of epilepsy. Adjusting for socio-economic status factors did not significantly change results. CONCLUSIONS: In childhood epilepsy various seizure-related factors, but not SES factors, were associated with the presence or absence of SNVP. Such deficits may be especially common in children with remote symptomatic epilepsy aetiology and in complex and therapy resistant epilepsies. Low frequencies of SNVP may be found in children with idiopathic and LR cryptogenic epilepsy syndromes, simple partial or absence seizures and a late epilepsy debut. Our study contributes to an overall picture of cognitive function and its relation to central seizure characteristics in a childhood epilepsy population and can be useful for the follow-up team in developing therapy strategies that meet the individual needs of the child with epilepsy.

Adolescent↗
PubMed2004

Sociality and the evolution of intelligence.

Two recently published studies provide important new data relevant to the evolution of human intelligence. Both studies of social behavior in baboons, Bergman et al. demonstrated that baboons use two criteria simultaneously to classify other troop members, and Silk et al. showed that highly social female baboons have higher reproductive success than less social females. Taken together, these studies provide strong evidence for the importance of social context in cognitive evolution.

Animals↗
PubMed2006

Questioning the social intelligence hypothesis.

The social intelligence hypothesis posits that complex cognition and enlarged "executive brains" evolved in response to challenges that are associated with social complexity. This hypothesis has been well supported, but some recent data are inconsistent with its predictions. It is becoming increasingly clear that multiple selective agents, and non-selective constraints, must have acted to shape cognitive abilities in humans and other animals. The task now is to develop a larger theoretical framework that takes into account both inter-specific differences and similarities in cognition. This new framework should facilitate consideration of how selection pressures that are associated with sociality interact with those that are imposed by non-social forms of environmental complexity, and how both types of functional demands interact with phylogenetic and developmental constraints.

Animals↗
PubMed2004

Estimation of some transducer parameters in a broadband piezoelectric transmitter by using an artificial intelligence technique.

An estimation procedure to efficiently find approximate values of internal parameters in ultrasonic transducers intended for broadband operation would be a valuable tool to discover internal construction data. This information is necessary in the modelling and simulation of acoustic and electrical behaviour related to ultrasonic systems containing commercial transducers. There is not a general solution for this generic problem of parameter estimation in the case of broadband piezoelectric probes. In this paper, this general problem is briefly analysed for broadband conditions. The viability of application in this field of an artificial intelligence technique supported on the modelling of the transducer internal components is studied. A genetic algorithm (GA) procedure is presented and applied to the estimation of different parameters, related to two transducers which are working as pulsed transmitters. The efficiency of this GA technique is studied, considering the influence of the number and variation range of the estimated parameters. Estimation results are experimentally ratified.

Algorithms↗
PubMed1998

An artificial intelligence system for computer-assisted menu planning.

Planning nutritious and appetizing menus is a complex task that researchers have tried to computerize since the early 1960s. We have attempted to facilitate computer-assisted menu planning by modeling the reasoning an expert dietitian uses to plan menus. Two independent expert systems were built, each designed to plan a daily menu meeting the nutrition needs and personal preferences of an individual client. One system modeled rule-based, or logical, reasoning, whereas the other modeled case-based, or experiential, reasoning. The 2 systems were evaluated and their strengths and weaknesses identified. A hybrid system was built, combining the best of both systems. The hybrid system represents an important step forward because it plans daily menus in accordance with a person's needs and preferences; the Reference Daily Intakes; the Dietary Guidelines for Americans; and accepted aesthetic standards for color, texture, temperature, taste, and variety. Additional work to expand the system's scope and to enhance the user interface will be needed to make it a practical tool. Our system framework could be applied to special-purpose menu planning for patients in medical settings or adapted for institutional use. We conclude that an artificial intelligence approach has practical use for computer-assisted menu planning.

Artificial Intelligence↗
PubMed1997

Cardiac risk stratification in renal transplantation using a form of artificial intelligence.

The purpose of this study was to determine if an expert network, a form of artificial intelligence, could effectively stratify cardiac risk in candidates for renal transplant. Input into the expert network consisted of clinical risk factors and thallium-201 stress test data. Clinical risk factor screening alone identified 95 of 189 patients as high risk. These 95 patients underwent thallium-201 stress testing, and 53 had either reversible or fixed defects. The other 42 patients were classified as low risk. This algorithm made up the "expert system," and during the 4-year follow-up period had a sensitivity of 82%, specificity of 77%, and accuracy of 78%. An artificial neural network was added to the expert system, creating an expert network. Input into the neural network consisted of both clinical variables and thallium-201 stress test data. There were 5 hidden nodes and the output (end point) was cardiac death. The expert network increased the specificity of the expert system alone from 77% to 90% (p < 0.001), the accuracy from 78% to 89% (p < 0.005), and maintained the overall sensitivity at 88%. An expert network based on clinical risk factor screening and thallium-201 stress testing had an accuracy of 89% in predicting the 4-year cardiac mortality among 189 renal transplant candidates.

Adult↗
PubMed1989

Use of artificial intelligence for the preoperative diagnosis of pulmonary lesions.

The relatively new field of artificial intelligence has spawned a variety of techniques associated with computer-assisted diagnosis. These techniques have been applied to the diagnosis of pulmonary lesions, but previous reports have focused on medical rather than surgical populations and the results have been evaluated using only retrospective patient surveys. We used a Bayesian algorithm to develop a diagnostic computer model for prospectively evaluating patients undergoing thoracotomy for suspected pulmonary malignancy. Patients who had a preoperative diagnosis were not included. Preoperative clinical and radiographic parameters for 100 consecutive patients were prospectively entered into the diagnostic model, which then categorized the lesion as benign or malignant. The computer predictions agreed with the final histological diagnosis in 95 of the 100 patients. The sensitivity was 96% and the specificity was 89% for this prospective series. These results indicate that the computer-assisted diagnosis of pulmonary lesions may have a role in this clinical setting.

Adult↗
PubMed1999

Psychometric intelligence in patients with traumatic brain injury: utility of a new screening measure.

OBJECTIVE: To evaluate the validity of the General Ability Measure for Adults (GAMA) in a sample of patients with traumatic brain injury (TBI). DESIGN: Comparison with criterion standards of Wechsler Adult Intelligence Scale-Revised (WAIS-R) and measures of injury severity. SETTING: Regional rehabilitation center. PARTICIPANTS: One-year prospective series of consecutive rehabilitation referrals, including 42 adult patients with TBI. MAIN OUTCOME MEASURES: GAMA and WAIS-R IQ scores were obtained within 1 year after injury and compared with each other and with measures of injury severity. RESULTS: The GAMA demonstrated statistically significant covariance with the WAIS-R and was able to discriminate severe TBI from mild/moderate TBI. The correlation between the GAMA and length of coma fell just short of statistical significance. CONCLUSIONS: The GAMA is sufficiently sensitive to the presence or absence of severe TBI but may not be sufficiently sensitive to the exact degree of injury severity.

Adolescent↗
PubMed2001

Intelligence quotient and neuropsychological profiles in patients with schizophrenia and in normal volunteers.

BACKGROUND: The objective of this study was to examine neuropsychological performance at different intelligence quotient (IQ) levels in schizophrenia. METHODS: Thirty-six patients with schizophrenia were matched with 36 normal control subjects in two IQ groups: low average (81-94) and average (95-119). Performance level (IQ group main effects) and profile shape (IQ group x function interactions) were compared. RESULTS: Current IQ was lower than estimated premorbid intellectual ability in both patient groups. Patients also displayed poorer neuropsychological function than same-IQ control subjects, suggesting neuropsychological dysfunction beyond their already compromised IQ. Patients had different profile shapes than control subjects, but profile shapes were consistent within patients and control subjects at each IQ level. Patients at both levels had higher verbal and lower performance IQ than control subjects. Abstraction-executive function was one of the lowest neuropsychological scores in both patient groups. Average IQ patients had nonsignificantly better overall neuropsychological performance than low average control subjects, but the effect size (.43) was quite small relative to the IQ difference (effect size = 2.57). CONCLUSIONS: Neuropsychological patterns in schizophrenia tend to be consistent at different IQ levels. Even schizophrenia patients with normal current IQs manifest substantial neuropsychological compromise relative to their level of general intellectual ability. The results strengthen the argument that neurocognitive deficits are core deficits of schizophrenic illness.

Adult↗
PubMed2003

An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks.

In this paper, an intelligent system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective in detecting Doppler heart sounds. The correct classification rate was about 94% for abnormal and normal subjects.

Artificial Intelligence↗
PubMed2004

Intelligent training system integrated in an echocardiography simulator.

Computer simulators play an important role in medical education. We have extended our simulator EchoComJ with an intelligent training system (ITS) to support trainees adjusting echocardiographic standard views. EchoComJ is an augmented reality application that combines real three-dimensional ultrasound data with a virtual heart model enabling one to simulate an echocardiographic examination. The ITS analyzes the image planes according to their position, orientation and the visualization of anatomical landmarks using fuzzy rules. An adaptive feedback is provided that colors the specific anatomic landmarks within the contours of the virtual model based on the quality of the image plane.

Artificial Intelligence↗
PubMed1975

Right-left discrimination skills of hemiplegic persons of limited intelligence.

Past research has indicated a relationship between degree of unilateral usage of body parts and right-left discrimination skills. Further evidence for such an association was sought by comparing the scores of hemiplegic persons of limited intelligence on a test of rather advanced righ-left discrimination skills devised by the authors to those earned by a control group of non-hemiplegic persons matched on the variables of age, sex, race, and IQ. Mean scores of the hemiplegic group were significantly higher than those of the control group. Seventeen out of 21 hemiplegic persons scored higher than their matched controls. Results are discussed in terms of a possible causal relationship between perceived visual and kinesthetic differences between the right and left sides of one's own body and right-left discrimination in external space. Lesser degrees of inter-hemispheric communication among hemiplegic persons also are mentioned as a possible causal factor.

Adolescent↗
PubMed1978

Handedness and intelligence.

The scores of 37 right and 30 left-handed subjects on tests of fluid and crystallized intelligence were compared. Consistent with the hypothesis, it was found that the left-handers were inferior to the right-handers on the Gf task. These data are consistent with those from three earlier studies and serve to question the assertion that there are no handedness related differences in ability.

Functional Laterality↗
PubMed1984

Handedness, sex and intelligence.

On the basis of data from a large and representative population (N = 1880), we have now had the opportunity to examine the relationships that have been claimed to exist between handedness, sex, and the patterning of intellectual abilities, as these are reflected in performance on the Wechsler Adult Intelligence Scale--Revised (Wechsler, 1981). Such relationships had previously been studied using rather small samples of men and women. Our analyses show a reliable, if negligible, effect of sex on these test results, but no effect of handedness.

Adult↗
PubMed2006

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks.

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.

Aircraft↗